cd ../..

/code

%run "source/config/notebook_settings.py"
import os
import mlflow
from mlflow.tracking import MlflowClient
from helpsk.utility import read_pickle
import helpsk as hlp

from source.library.utilities import Timer, log_info, get_config

config = get_config()
mlflow_uri = config['MLFLOW']['URI']
log_info(f"MLFlow URI: {mlflow_uri}")

client = MlflowClient(tracking_uri=mlflow_uri)

2022-06-20 02:55:11 - INFO     | MLFlow URI: http://mlflow_server:1235

Get Latest Experiment Run from MLFlow

# Get the production model version and actual model
production_model_info = client.get_latest_versions(name=config['MLFLOW']['MODEL_NAME'], stages=['Production'])
assert len(production_model_info) == 1
production_model_info = production_model_info[0]
production_model = read_pickle(client.download_artifacts(
    run_id=production_model_info.run_id,
    path='model/model.pkl'
))
log_info(f"Production Model Version: {production_model_info.version}")

2022-06-20 02:55:12 - INFO     | Production Model Version: 1

# get experiment and latest run info
credit_experiment = client.get_experiment_by_name(name=config['MLFLOW']['EXPERIMENT_NAME'])
runs = client.list_run_infos(experiment_id=credit_experiment.experiment_id)
latest_run = runs[np.argmax([x.start_time for x in runs])]

yaml_path = client.download_artifacts(run_id=latest_run.run_id, path='experiment.yaml')
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = yaml_path)

# get the best estimator from the BayesSearchCV
best_estimator = read_pickle(client.download_artifacts(
    run_id=latest_run.run_id,
    path='model/model.pkl'
))

best_estimator.model

Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer())),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser())]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credits',
                                                   'num_dependents']),
                                                 ('non_numeric',
                                                  Pipeline(steps...,
                                                                   TransformerChooser(transformer=OneHotEncoder(handle_unknown='ignore')))]),
                                                  ['checking_status',
                                                   'credit_history', 'purpose',
                                                   'savings_status',
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker'])])),
                ('model',
                 RandomForestClassifier(n_estimators=500, random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Training & Test Data Info

client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl')

'/code/mlflow-artifact-root/1/902c7e083e1b46a18adee49807be002f/artifacts/x_train.pkl'

with Timer("Loading training/test datasets"):
    X_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl'))
    X_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_test.pkl'))
    y_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_train.pkl'))
    y_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_test.pkl'))

2022-06-20 02:55:12 - INFO     | Timer Started: Loading training/test datasets
2022-06-20 02:55:12 - INFO     | Timer Finished: (0.02 seconds)

log_info(X_train.shape)
log_info(len(y_train))

log_info(X_test.shape)
log_info(len(y_test))

2022-06-20 02:55:12 - INFO     | (800, 20)
2022-06-20 02:55:12 - INFO     | 800
2022-06-20 02:55:12 - INFO     | (200, 20)
2022-06-20 02:55:12 - INFO     | 200

np.unique(y_train, return_counts=True)

(array([0, 1]), array([559, 241]))

np.unique(y_train, return_counts=True)[1] / np.sum(np.unique(y_train, return_counts=True)[1])

array([0.69875, 0.30125])

np.unique(y_test, return_counts=True)[1] / np.sum(np.unique(y_test, return_counts=True)[1])

array([0.705, 0.295])

Cross Validation Results

Best Scores/Params

log_info(f"Best Score: {results.best_score}")

2022-06-20 02:55:12 - INFO     | Best Score: 0.7566610156276207

log_info(f"Best Params: {results.best_params}")

2022-06-20 02:55:12 - INFO     | Best Params: {'model': 'RandomForestClassifier()', 'imputer': 'SimpleImputer()', 'scaler': 'None', 'pca': 'None', 'encoder': 'OneHotEncoder()'}

# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')

	rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder	model_rank
10	1	0.76	0.65	0.86	RandomForestClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	None	OneHotEncoder()	1.00
2	3	0.75	0.69	0.80	LogisticRegression()	0.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()	1.00
5	7	0.75	0.67	0.83	ExtraTreesClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	None	OneHotEncoder()	1.00
19	9	0.74	0.64	0.84	XGBClassifier()	NaN	NaN	1.00	1315.00	NaN	NaN	NaN	NaN	0.02	17.00	0.72	0.53	0.99	0.00	2.78	NaN	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()	1.00
21	12	0.74	0.66	0.81	LGBMClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.63	0.25	NaN	5.83	44.98	347.00	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()	1.00

results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder
1	0.757	0.650	0.864	RandomForestClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
2	0.750	0.646	0.855	RandomForestClassifier()	<NA>	0.239	41.000	1,886.000	3.000	15.000	0.864	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	OneHotEncoder()
3	0.749	0.695	0.804	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
4	0.749	0.619	0.879	RandomForestClassifier()	<NA>	0.583	35.000	1,474.000	22.000	5.000	0.765	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	CustomOrdinalEncoder()
5	0.749	0.695	0.803	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
6	0.749	0.694	0.804	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	MinMaxScaler()	PCA('mle')	OneHotEncoder()
7	0.748	0.665	0.831	ExtraTreesClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
8	0.744	0.641	0.847	LogisticRegression()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
9	0.741	0.641	0.841	XGBClassifier()	<NA>	<NA>	1.000	1,315.000	<NA>	<NA>	<NA>	<NA>	0.022	17.000	0.716	0.530	0.986	0.002	2.778	<NA>	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
10	0.740	0.640	0.839	RandomForestClassifier()	<NA>	0.445	87.000	1,244.000	33.000	27.000	0.795	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	OneHotEncoder()
11	0.738	0.621	0.855	RandomForestClassifier()	<NA>	0.911	74.000	1,265.000	39.000	17.000	0.751	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	OneHotEncoder()
12	0.737	0.661	0.813	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.630	0.248	<NA>	5.830	44.985	347.000	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
13	0.733	0.597	0.870	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.732	0.746	<NA>	0.877	47.642	497.000	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
14	0.732	0.682	0.782	ExtraTreesClassifier()	<NA>	0.502	29.000	1,249.000	25.000	35.000	0.855	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
15	0.728	0.695	0.760	ExtraTreesClassifier()	<NA>	0.135	15.000	1,987.000	10.000	39.000	0.708	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
16	0.725	0.635	0.815	ExtraTreesClassifier()	<NA>	0.768	54.000	909.000	16.000	30.000	0.762	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	None	OneHotEncoder()
17	0.725	0.627	0.823	XGBClassifier()	<NA>	<NA>	3.000	1,482.000	<NA>	<NA>	<NA>	<NA>	0.067	18.000	0.889	0.636	0.615	0.000	2.093	<NA>	SimpleImputer()	None	None	CustomOrdinalEncoder()
18	0.723	0.623	0.823	ExtraTreesClassifier()	<NA>	0.537	2.000	1,275.000	14.000	47.000	0.805	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
19	0.723	0.628	0.817	XGBClassifier()	<NA>	<NA>	20.000	1,733.000	<NA>	<NA>	<NA>	<NA>	0.010	2.000	0.673	0.772	0.881	0.281	2.005	<NA>	SimpleImputer(strategy='median')	None	None	OneHotEncoder()
20	0.713	0.604	0.823	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
21	0.710	0.663	0.758	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.429	0.877	<NA>	13.997	35.364	388.000	SimpleImputer()	None	None	CustomOrdinalEncoder()
22	0.705	0.674	0.736	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.965	0.241	<NA>	19.400	3.771	281.000	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
23	0.702	0.659	0.745	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	MinMaxScaler()	None	CustomOrdinalEncoder()
24	0.699	0.610	0.788	XGBClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
25	0.689	0.555	0.823	XGBClassifier()	<NA>	<NA>	4.000	1,961.000	<NA>	<NA>	<NA>	<NA>	0.271	3.000	0.671	0.797	0.696	0.000	2.150	<NA>	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()

results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	imputer	pca	encoder
1	0.757	0.650	0.864	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	OneHotEncoder()
2	0.750	0.646	0.855	0.239	41.000	1,886.000	3.000	15.000	0.864	gini	SimpleImputer(strategy='most_frequent')	None	OneHotEncoder()
3	0.749	0.619	0.879	0.583	35.000	1,474.000	22.000	5.000	0.765	entropy	SimpleImputer(strategy='most_frequent')	PCA('mle')	CustomOrdinalEncoder()
4	0.740	0.640	0.839	0.445	87.000	1,244.000	33.000	27.000	0.795	gini	SimpleImputer(strategy='most_frequent')	None	OneHotEncoder()
5	0.738	0.621	0.855	0.911	74.000	1,265.000	39.000	17.000	0.751	entropy	SimpleImputer(strategy='most_frequent')	None	OneHotEncoder()

results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	C	imputer	scaler	pca	encoder
1	0.749	0.695	0.804	0.000	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
2	0.749	0.695	0.803	0.000	SimpleImputer(strategy='most_frequent')	MinMaxScaler()	None	OneHotEncoder()
3	0.749	0.694	0.804	0.000	SimpleImputer()	MinMaxScaler()	PCA('mle')	OneHotEncoder()
4	0.744	0.641	0.847	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
5	0.702	0.659	0.745	0.000	SimpleImputer(strategy='median')	MinMaxScaler()	None	CustomOrdinalEncoder()

BayesSearchCV Performance Over Time

results.plot_performance_across_trials(facet_by='model').show()

results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

---

Variable Performance Over Time

results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

---

Scatter Matrix

# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

---

Variable Performance - Numeric

results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)

results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

---

Variable Performance - Non-Numeric

results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

---

results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)

---

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

---

Best Model - Test Set Performance

test_predictions = best_estimator.predict(X_test)
test_predictions[0:10]

array([0.388, 0.506, 0.724, 0.368, 0.056, 0.472, 0.076, 0.47 , 0.18 ,
       0.23 ])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.823	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.746	0.000	1.000	74.6% of positive instances were correctly identified.; i.e. 44 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.801	1.000	0.000	80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.199	0.000	1.000	19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.254	1.000	0.000	25.4% of positive instances were incorrectly identified as negative; i.e. 15 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.611	0.000	0.295	When the model claims an instance is positive, it is correct 61.1% of the time; i.e. out of the 72 times the model predicted "Positive Class", it was correct 44 times; a.k.a precision
Negative Predictive Value	0.883	0.705	0.000	When the model claims an instance is negative, it is correct 88.3% of the time; i.e. out of the 128 times the model predicted "Negative Class", it was correct 113 times
F1 Score	0.672	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.662	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.785	0.705	0.295	78.5% of instances were correctly identified
Error Rate	0.215	0.295	0.705	21.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.14	2.71
10	0.24	2.37
15	0.37	2.49
20	0.49	2.46
25	0.54	2.17
30	0.66	2.20
35	0.71	2.03
40	0.75	1.86
45	0.80	1.77
50	0.83	1.66
55	0.85	1.54
60	0.86	1.44
65	0.90	1.38
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---

Production Model - Test Set Performance

test_predictions = production_model.predict(X_test)
test_predictions[0:10]

array([0.388, 0.506, 0.724, 0.368, 0.056, 0.472, 0.076, 0.47 , 0.18 ,
       0.23 ])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.823	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.746	0.000	1.000	74.6% of positive instances were correctly identified.; i.e. 44 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.801	1.000	0.000	80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.199	0.000	1.000	19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.254	1.000	0.000	25.4% of positive instances were incorrectly identified as negative; i.e. 15 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.611	0.000	0.295	When the model claims an instance is positive, it is correct 61.1% of the time; i.e. out of the 72 times the model predicted "Positive Class", it was correct 44 times; a.k.a precision
Negative Predictive Value	0.883	0.705	0.000	When the model claims an instance is negative, it is correct 88.3% of the time; i.e. out of the 128 times the model predicted "Negative Class", it was correct 113 times
F1 Score	0.672	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.662	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.785	0.705	0.295	78.5% of instances were correctly identified
Error Rate	0.215	0.295	0.705	21.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.14	2.71
10	0.24	2.37
15	0.37	2.49
20	0.49	2.46
25	0.54	2.17
30	0.66	2.20
35	0.71	2.03
40	0.75	1.86
45	0.80	1.77
50	0.83	1.66
55	0.85	1.54
60	0.86	1.44
65	0.90	1.38
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

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